


'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class        lowercase__    (		_snake_case	,	_snake_case	,	_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Union[str, Any]       =    StableDiffusionInstructPixaPixPipeline
							A_				: List[Any]       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
							A_				: str       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							A_				: int       =    IMAGE_TO_IMAGE_IMAGE_PARAMS
							A_				: str       =    IMAGE_TO_IMAGE_IMAGE_PARAMS






							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=8       ,   out_channels=4       ,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,   cross_attention_dim=32       ,   )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= PNDMScheduler(skip_prk_steps=__snake_case    )
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= AutoencoderKL(
											    block_out_channels=[32, 64]       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,   latent_channels=4       ,   )
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			int							= CLIPTextConfig(
											    bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=32       ,   intermediate_size=37       ,   layer_norm_eps=1e-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1000       ,   )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPTextModel(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

											_SCREAMING_SNAKE_CASE					:			str							= {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											return components






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=0    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= floats_tensor((1, 3, 32, 32)       ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image.cpu().permute(0       ,   2       ,   3       ,   1    )[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= Image.fromarray(np.uinta(__snake_case    )    ).convert("""RGB"""    )
											if str(__snake_case    ).startswith("""mps"""    ):
															_SCREAMING_SNAKE_CASE					:			Dict							= torch.manual_seed(__snake_case    )
											else:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 6.0,
											    """image_guidance_scale""": 1,
											    """output_type""": """numpy""",
											}
											return inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """cpu"""  # ensure determinism for the device-dependent torch.Generator
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_dummy_components()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= StableDiffusionInstructPixaPixPipeline(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= sd_pipe.to(__snake_case    )
											sd_pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_dummy_inputs(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			str							= sd_pipe(**__snake_case    ).images
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image[0, -3:, -3:, -1]
											assert image.shape == (1, 32, 32, 3)
											_SCREAMING_SNAKE_CASE					:			int							= np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= """cpu"""  # ensure determinism for the device-dependent torch.Generator
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_dummy_components()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= StableDiffusionInstructPixaPixPipeline(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= sd_pipe.to(__snake_case    )
											sd_pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			int							= self.get_dummy_inputs(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """french fries"""
											_SCREAMING_SNAKE_CASE					:			List[str]							= sd_pipe(**__snake_case       ,   negative_prompt=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= output.images
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											_SCREAMING_SNAKE_CASE					:			str							= np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """cpu"""  # ensure determinism for the device-dependent torch.Generator
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_dummy_components()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= StableDiffusionInstructPixaPixPipeline(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= sd_pipe.to(__snake_case    )
											sd_pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= self.get_dummy_inputs(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= [inputs["""prompt"""]] * 2

											_SCREAMING_SNAKE_CASE					:			str							= np.array(inputs["""image"""]    ).astype(np.floataa    ) / 255.0
											_SCREAMING_SNAKE_CASE					:			Dict							= torch.from_numpy(__snake_case    ).unsqueeze(0    ).to(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= image / 2 + 0.5
											_SCREAMING_SNAKE_CASE					:			List[str]							= image.permute(0       ,   3       ,   1       ,   2    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= image.repeat(2       ,   1       ,   1       ,   1    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= sd_pipe(**__snake_case    ).images
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image[-1, -3:, -3:, -1]

											assert image.shape == (2, 32, 32, 3)
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """cpu"""  # ensure determinism for the device-dependent torch.Generator
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_dummy_components()
											_SCREAMING_SNAKE_CASE					:			Dict							= EulerAncestralDiscreteScheduler(
											    beta_start=0.00085       ,   beta_end=0.012       ,   beta_schedule="""scaled_linear"""    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= StableDiffusionInstructPixaPixPipeline(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= sd_pipe.to(__snake_case    )
											sd_pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_dummy_inputs(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= sd_pipe(**__snake_case    ).images
											_SCREAMING_SNAKE_CASE					:			int							= image[0, -3:, -3:, -1]

											_SCREAMING_SNAKE_CASE					:			Any							= [round(__snake_case       ,   4    ) for x in image_slice.flatten().tolist()]
											print(""",""".join([str(__snake_case    ) for x in slice]    )    )

											assert image.shape == (1, 32, 32, 3)
											_SCREAMING_SNAKE_CASE					:			str							= np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






							def UpperCAmelCase_      (       self    ):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_dummy_components()
											_SCREAMING_SNAKE_CASE					:			List[str]							= StableDiffusionInstructPixaPixPipeline(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= VaeImageProcessor(do_resize=__snake_case       ,   do_normalize=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			str							= pipe.to(__snake_case    )
											pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= pipe(**self.get_dummy_inputs_by_type(__snake_case       ,   input_image_type="""pt"""    )    )[0]

											_SCREAMING_SNAKE_CASE					:			str							= components["""vae"""]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_dummy_inputs_by_type(__snake_case       ,   input_image_type="""pt"""    )

											for image_param in self.image_latents_params:
															if image_param in inputs.keys():
																			_SCREAMING_SNAKE_CASE					:			List[str]							= vae.encode(inputs[image_param]    ).latent_dist.mode()

											_SCREAMING_SNAKE_CASE					:			Dict							= pipe(**__snake_case    )[0]

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= np.abs(out - out_latents_inputs    ).max()
											self.assertLess(__snake_case       ,   1e-4       ,   """passing latents as image input generate different result from passing image"""    )





@slow
@require_torch_gpu
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_      (       self       ,   __snake_case=0    ):
											_SCREAMING_SNAKE_CASE					:			str							= torch.manual_seed(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= load_image(
											    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= {
											    """prompt""": """turn him into a cyborg""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 3,
											    """guidance_scale""": 7.5,
											    """image_guidance_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""       ,   safety_checker=__snake_case    )
											pipe.to(__snake_case    )
											pipe.set_progress_bar_config(disable=__snake_case    )
											pipe.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE					:			int							= self.get_inputs()
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipe(**__snake_case    ).images
											_SCREAMING_SNAKE_CASE					:			Dict							= image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]    )

											assert np.abs(expected_slice - image_slice    ).max() < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""       ,   safety_checker=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
											pipe.to(__snake_case    )
											pipe.set_progress_bar_config(disable=__snake_case    )
											pipe.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_inputs()
											_SCREAMING_SNAKE_CASE					:			int							= pipe(**__snake_case    ).images
											_SCREAMING_SNAKE_CASE					:			Dict							= image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]    )

											assert np.abs(expected_slice - image_slice    ).max() < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""       ,   safety_checker=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			str							= DDIMScheduler.from_config(pipe.scheduler.config    )
											pipe.to(__snake_case    )
											pipe.set_progress_bar_config(disable=__snake_case    )
											pipe.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_inputs()
											_SCREAMING_SNAKE_CASE					:			str							= pipe(**__snake_case    ).images
											_SCREAMING_SNAKE_CASE					:			List[str]							= image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											_SCREAMING_SNAKE_CASE					:			str							= np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]    )

											assert np.abs(expected_slice - image_slice    ).max() < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= 0

											def callback_fn(__snake_case       ,   __snake_case       ,   __snake_case    ) -> None:
															_SCREAMING_SNAKE_CASE					:			Dict							= True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			_SCREAMING_SNAKE_CASE					:			Dict							= latents[0, -3:, -3:, -1]
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]    )

																			assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2
															elif step == 2:
																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= latents[0, -3:, -3:, -1]
																			_SCREAMING_SNAKE_CASE					:			List[str]							= np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]    )

																			assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= False

											_SCREAMING_SNAKE_CASE					:			Any							= StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""       ,   safety_checker=__snake_case       ,   torch_dtype=torch.floataa    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= pipe.to(__snake_case    )
											pipe.set_progress_bar_config(disable=__snake_case    )
											pipe.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_inputs()
											pipe(**__snake_case       ,   callback=__snake_case       ,   callback_steps=1    )
											assert callback_fn.has_been_called
											assert number_of_steps == 3






							def UpperCAmelCase_      (       self    ):
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											_SCREAMING_SNAKE_CASE					:			int							= StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""       ,   safety_checker=__snake_case       ,   torch_dtype=torch.floataa    )
											_SCREAMING_SNAKE_CASE					:			Any							= pipe.to(__snake_case    )
											pipe.set_progress_bar_config(disable=__snake_case    )
											pipe.enable_attention_slicing(1    )
											pipe.enable_sequential_cpu_offload()

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_inputs()
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipe(**__snake_case    )

											_SCREAMING_SNAKE_CASE					:			int							= torch.cuda.max_memory_allocated()
											# make sure that less than 2.2 GB is allocated
											assert mem_bytes < 2.2 * 10**9






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= self.get_inputs()
											# resize to resolution that is divisible by 8 but not 16 or 32
											_SCREAMING_SNAKE_CASE					:			Tuple							= inputs["""image"""].resize((504, 504)    )

											_SCREAMING_SNAKE_CASE					:			int							= """timbrooks/instruct-pix2pix"""
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    __snake_case       ,   safety_checker=__snake_case       ,   )
											pipe.to(__snake_case    )
											pipe.set_progress_bar_config(disable=__snake_case    )
											pipe.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE					:			int							= pipe(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Any							= output.images[0]

											_SCREAMING_SNAKE_CASE					:			List[Any]							= image[255:258, 383:386, -1]

											assert image.shape == (504, 504, 3)
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3








'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return "".join(chr(ord(SCREAMING_SNAKE_CASE__	) - 32	) if """a""" <= char <= """z""" else char for char in word	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




